5.1 CATS Cloud-Aerosol Applications and Data Product Performance

Tuesday, 24 January 2017: 1:30 PM
Conference Center: Skagit 4 (Washington State Convention Center )
John E. Yorks, NASA, Greenbelt, MD; and M. J. McGill, S. P. Palm, D. L. Hlavka, E. P. Nowottnick, P. Selmer, R. M. Pauly, S. Rodier, M. A. Vaughan, N. Midzak, and S. Ozog

The Cloud-Aerosol Transport System (CATS) is an elastic backscatter lidar that has been operating on the International Space Station (ISS) since February 2015. CATS was designed to demonstrate new in-space technologies for future Earth Science missions while also providing vertical profiles of clouds and aerosols. The ISS orbit provides more comprehensive coverage of the tropics and mid-latitudes than the A-Train sensors, with nearly a three-day repeat cycle. Thus, CATS fills in the spatial and temporal gaps between the twice-daily CALIPSO measurements.

With over 18 months of operation from the ISS, CATS data has applications such as detection frequency of cloud and aerosol types, properties of aerosols above and near clouds, plume tracking with combined CATS-CALIPSO data, and studies of cloud and aerosol diurnal variability. Additionally, CATS data products are currently being processed in near real time (less than six hours) for applications such as forecasting of volcanic plume transport, experimental data assimilation into aerosol transport models (GEOS-5, NAAPS), and field campaign flight planning (KORUS-AQ, ORACLES).

Level 1B (L1B) Version 2-07 and Level 2 (L2) Version 1-04 data products were released in June 2016. The CATS V2-07 1064 nm attenuated backscatter agrees favorably with airborne Cloud Physics Lidar (CPL) data, suggesting the CATS L1B products are well calibrated at that wavelength. Evaluation of L2 V1-04 data does yield several biases in cloud and aerosol layer detection and identification, as well as retrievals of optical properties that will be improved for the next version to be released in late 2016. Results using CATS data products will be presented, as well as future improvements to algorithms and data products.

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